A Y-shape-structured electrochemiluminescence biosensor based on carbon quantum dots and locked nucleic acid probe for microRNA determination with single-base resolution.


Journal

Biosensors & bioelectronics
ISSN: 1873-4235
Titre abrégé: Biosens Bioelectron
Pays: England
ID NLM: 9001289

Informations de publication

Date de publication:
15 Oct 2023
Historique:
received: 26 05 2023
revised: 24 07 2023
accepted: 07 08 2023
medline: 28 8 2023
pubmed: 14 8 2023
entrez: 13 8 2023
Statut: ppublish

Résumé

Since microRNAs (miRNAs) are predictors of tumorigenesis, accurate identification and quantification of miRNAs with highly similar sequences are expected to reflect tumor diagnosis and treatment. In this study, a highly selective and sensitive electrochemiluminescence (ECL) biosensor was constructed for miRNAs determination based on Y-shaped junction structure equipped with locked nucleic acids (LNA), graphene oxide-based nanocomposite to enrich luminophores, and conductive matrix. Specifically, two LNA-modified probes were designed for specific miRNA recognition, that is, a dual-amine functionalized hairpin capture probe and a signal probe. A Y-shaped DNA junction structure was generated on the electrode surface upon miRNA hybridizing across the two branches, so as to enhance the selectivity. Carbon quantum dots-polyethylene imine-graphene oxide (CQDs-PEI-GO) nanocomposites were developed to enrich luminophores CQDs, and thus enhancing the ECL intensity. For indirect signal amplification, an electrochemically activated poly(2-aminoterephthalic acid) (ATA) film decorated with gold nanoparticles was prepared on electrode as an effective matrix to accelerate the electron transfer. The fabricated ECL biosensor achieved sensitive determination of miRNA-222 with a limit-of-detection (LOD) as low as 1.95 fM (S/N = 3). Notably, Y-shaped junction structures equipped with LNA probes endowed ECL biosensor with salient single-base discrimination ability and anti-interference capacity. Overall, the proposed Y-shaped ECL biosensor has considerable promise for clinical biomarker determination.

Identifiants

pubmed: 37573643
pii: S0956-5663(23)00525-0
doi: 10.1016/j.bios.2023.115583
pii:
doi:

Substances chimiques

MicroRNAs 0
graphene oxide 0
Carbon 7440-44-0
locked nucleic acid 0
Gold 7440-57-5
DNA 9007-49-2
Nucleic Acid Probes 0
Polyethyleneimine 9002-98-6

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

115583

Informations de copyright

Copyright © 2023 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Yu Zhong (Y)

Department of Pharmaceutical Analysis, Higher Educational Key Laboratory for Nano Biomedical Technology of Fujian Province, Faculty of Pharmacy, Fujian Medical University, Fuzhou, 350122, China.

Lin-Xiao Huang (LX)

Department of Pharmaceutical Analysis, Higher Educational Key Laboratory for Nano Biomedical Technology of Fujian Province, Faculty of Pharmacy, Fujian Medical University, Fuzhou, 350122, China.

Mu-Tu Lin (MT)

Department of Pharmaceutical Analysis, Higher Educational Key Laboratory for Nano Biomedical Technology of Fujian Province, Faculty of Pharmacy, Fujian Medical University, Fuzhou, 350122, China.

Zi-Yang Zhang (ZY)

Department of Pharmaceutical Analysis, Higher Educational Key Laboratory for Nano Biomedical Technology of Fujian Province, Faculty of Pharmacy, Fujian Medical University, Fuzhou, 350122, China.

Ai-Lin Liu (AL)

Department of Pharmaceutical Analysis, Higher Educational Key Laboratory for Nano Biomedical Technology of Fujian Province, Faculty of Pharmacy, Fujian Medical University, Fuzhou, 350122, China. Electronic address: ailinliu@fjmu.edu.cn.

Yun Lei (Y)

Department of Pharmaceutical Analysis, Higher Educational Key Laboratory for Nano Biomedical Technology of Fujian Province, Faculty of Pharmacy, Fujian Medical University, Fuzhou, 350122, China. Electronic address: lypiglet@163.com.

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Classifications MeSH